AI 中文总结
研究利用机器学习预测二维Mo-W-S-Se-Te过渡金属二卤化物合金光学性质，结合从头算与表格基础模型回归，通过物理信息采样策略训练TabPFN，能高精度预测介电函数及衍生光学量，还可零样本推广到多种合金成分。
AI 中文摘要
二维过渡金属二卤化物（TMD）合金为控制光学和电子性质提供了一个成分可调的平台。然而，由于第一性原理计算的组合成本，系统预测其在多组分合金空间中的介电响应仍然具有挑战性。在这项工作中，我们将从头算光学性质计算与表格基础模型回归相结合，以预测Mo-W-S-Se-Te TMD合金频率相关介电函数的实部和虚部。使用密度泛函理论（DFT）生成了一个包含99种合金结构的数据集，涵盖二元、三元、四元及五元成分。由此产生的偏振相关介电光谱用于训练表格先验拟合网络（TabPFN），并与传统的Extra Trees和XGBoost模型进行评估。为了适应TabPFN的上下文容量限制，我们引入了一种非均匀的、基于物理信息的能量子采样策略，该策略将采样集中在带隙以上的光学活性区域，其中带间吸收最强。仅在四元合金上训练的TabPFN重建了保留的四元成分的介电光谱，所有四个介电成分的R2>0.98，平均绝对误差低于0.10，优于两个基线，同时不需要基于梯度的训练或超参数调整。我们的模型还预测了包括折射率、消光系数和吸收系数在内的衍生光学量。此外，我们的模型以零样本方式推广到训练集中不存在的二元、三元和五元合金，五元预测的R2>0.97。
英文摘要
Two-dimensional transition-metal dichalcogenide (TMD) alloys provide a compositionally tunable platform for controlling the optical and electronic properties. However, systematic prediction of their dielectric response across multicomponent alloy spaces remains challenging owing to the combinatorial cost of first-principles calculations. In this work, we combined ab initio optical-property calculations with a tabular foundation-model regression to predict the real and imaginary components of the frequency-dependent dielectric function for Mo-W-S-Se-Te TMD alloys. A dataset of 99 alloy structures spanning binary, ternary, quaternary, and quinary compositions was generated using density functional theory (DFT). The resulting polarization-dependent dielectric spectra were used to train a tabular prior-fitted network (TabPFN) and evaluated against the conventional Extra Trees and XGBoost models. To accommodate the in-context capacity limit of TabPFN, we introduced a non-uniform, physics-informed energy subsampling strategy that concentrates sampling in the optically active region above the band gap, where interband absorption is strongest. Trained solely on quaternary alloys, our TabPFN reconstructed the dielectric spectra of held-out quaternary compositions with an R2 > 0.98 and a mean absolute error below 0.10 for all four dielectric components, outperforming both baselines while requiring no gradient-based training or hyperparameter tuning. Our model further predicted derived optical quantities, including refractive index, extinction coefficient, and absorption coefficient. Additionally, our model generalized in a zero-shot manner to binary, ternary, and quinary alloys absent from the training set, with quinary predictions achieving an R2 > 0.97.
